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Mean shift: A robust approach toward feature space analysis (2002)

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by Dorin Comaniciu , Peter Meer
Venue:In PAMI
Citations:2355 - 37 self
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BibTeX

@INPROCEEDINGS{Comaniciu02meanshift:,
    author = {Dorin Comaniciu and Peter Meer},
    title = {Mean shift: A robust approach toward feature space analysis},
    booktitle = {In PAMI},
    year = {2002},
    pages = {603--619}
}

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Abstract

A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Keyphrases

mean shift    feature space analysis    robust approach    image segmentation    nadaraya watson estimator    discrete data    old pattern recognition procedure    color image    mean shift procedure    low-level vision task    kernel regression    general nonparametric technique    underlying density function    basic computational module    robust m-estimators    gray level    extensive experimental result    recursive mean shift procedure    stationary point    complex multimodal feature space    shaped cluster    excellent performance   

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